The effective and safe administration of pharmaceutical products to patients, particularly in the context of determining an appropriate drug therapy following any clinical diagnosis, has become an evermore complex and challenging task for the modern healthcare professional. In particular, as clinical diagnoses and medical treatment becomes more sophisticated, the number of patients and indeed the number of illnesses or clinical indications, becomes proportionately larger, particularly in the case of expanded aged population growth. Further, as clinical indications (e.g., genetic variability in treatment guidelines) become better understood, a growing number of drugs and/or pharmaceuticals is becoming available for treatment of increasingly specific medical conditions.
With an ever growing number of pharmaceuticals products becoming available, so too is there an ever increasing amount of information associated with each of these pharmaceuticals products. Physicians are becoming overwhelmed with information of the type that is often critical for appropriate patient care. Examples of such information forms include consensus guidelines for patients with particular diseases, guidelines for specific medications and diagnostic screening guidelines for particular diseases and demographics. Other informational-type examples include clinical data on possible interactions between particular drugs, interactions between drugs and particular diseases, cross-allergies between drugs, and a voluminous set of diagnostic possibilities (often described as differential diagnoses) which need to be considered when a patient is associated with a lab test abnormality, physical finding, or particular complaint. Lack of mastery of these types of information, at the point of care, can lead to lethal or irreversible negative consequences to patient health or recovery prognosis.
Contemporary physicians are often presented with a choice between two less than optimal options; firstly, to attempt to memorize sufficient amounts of this type of information or, secondly, by referring to technical references (paper or electronic) as the need arises. These options are less than optimal in that memorization of all required information to practice state-of-the-art healthcare is formidable, if not impossible, for an ordinary human being. Further, exhaustive reference searches for specifically-needed information is not practical, given the average amount of time a physician is able to expend on any one particular patient. With regard to informational reference searches, the information must be actively sought after and is not generally found in any one particular reference set. References might include medical textbooks, journal articles, and other scientific publications, but might also include bulletins and notices periodically published by the various pharmaceuticals companies themselves, the U.S. Food and Drug Administration, insurance companies, and other similar entities. The physician must not only know what to look for, but also know where to look.
The above noted deficiencies in clinical information acquisition and publication is particularly troublesome in the development of appropriate drug therapy regimens. Developing an appropriate drug therapy, following any diagnosis, is an extremely complicated task and requires a physician to simultaneously consider the interaction characteristics of a large number of relevant clinical factors (termed herein as patient dimensions). In the current state of the art, a limited number of relatively crude, clinically incomplete decision support systems currently exist. These are generally classified as either drug interaction checkers or drug-metabolism impairment checkers.
For example, in the case of a drug-drug interaction checker, the decision support systems conventionally utilize a table of pre-defined possible drug-drug interactions and enable a physician to enter two or more drugs, in order to see if an interaction is expected within the parameters of the system. Drug X and drug Y, when used together, can cause undesirable side effects. However, drug X may have no interaction with drug Y, but drug X might cause serious harm if a patient has a co-existing disease Z. Thus, drug-drug interaction checkers are unable to make any determination with regard to a drug-disease interaction. For example, the heartburn drug Propulsid™ was withdrawn from the market because it was found to be potentially fatal when used in patients with a heart rhythm abnormality known as prolonged QT Syndrome. A simple drug-drug interaction checker would be unable to provide an alert to a physician on the basis of a drug-disease interaction. Importantly, interaction checking alone is not sufficient decision support for today's modern physician, because from the doctor's perspective, interaction warnings represent only potential problems, not potential solutions. As an example of this limitation, consider the MD who wishes to find which antibiotics are considered the most efficacious, in a specific geographical region, to treat a pneumonia caused by a specific strain of bacterium. Clearly an interaction checker alone is insufficient for the purposes of answering this question.
Drug metabolism impairment checkers represent another single-dimension decision support system which is utilized for patient-specific drug therapy and is exemplified by metabolism impairment due to liver or kidney disease, the organs which eliminate drugs from the body. In patient's with advanced age, or with reduced liver or kidney function, many drugs are metabolized by the body at a reduced rate. Accordingly, with impaired kidney or liver function, or extremes of age, drug doses need to be frequently reduced in order to avoid over-dosing. Dosage adjustments, necessary if the drug is to be metabolized by either organ, is determined according to well-defined tables which correlate the dose adjustment against common indices of degree of liver or kidney impairment.
In particular, U.S. Pat. No. 5,833,599, entitled “Providing Patient-Specific Drug Information” is directed to this most simplistic form of drug administration decision support, namely the ability to calculate drug dosage adjustments when elimination or metabolism of a drug is decreased specifically due to liver or kidney dysfunction. This particular reference describes a system which determines a modified dosage and/or alternative therapy on the basis of certain patient-provided information, including the patient's age, kidney function and liver function.
In addition to being relatively simplistic and directed to specific dysfunctions (i.e., liver or kidney impairment) there are a number of possible adverse interactions involving drugs which do not relate to specific metabolism dysfunctions or relate to metabolism impairment of which kidney disease, liver disease, or advanced age, are only a minute subset. For example, certain drugs need dose adjustment when other drugs are concurrently taken, when patients are smokers, when certain laboratory test abnormalities are noted. In addition, some drugs can be dangerous during pregnancy or breastfeeding (conditions and not diseases or dysfunction), while quite safe otherwise. Also, certain drugs can be dangerous in the context of disease which is completely unrelated to the metabolism of that drug. For example, patients with marked thrombocytopenia (a decreased number of platelets, which are responsible for the clotting of blood) should never receive the blood thinner medication called Coumarin, as the resulting combination can cause spontaneous bleeding into the brain, often resulting in stroke. In this particular scenario, the clinical condition of thrombocytopenia is completely unrelated to the metabolism of Coumarin by the liver, kidney, or age-related factors.
It should be understood that prior art-type drug-drug and drug-metabolic interaction tables and/or electronic systems which embody them, are very limited in nature, and incomplete in terms of considering all clinically important patient dimensions simultaneously. For example, if a person possesses the gene BRCA1 in his/her DNA, or is a heavy smoker, dosage adjustments (or abandonment of drug therapy all together) may be warranted for particular drug therapy regimens. In no system existing today can all of the relevant clinical dimensions impinging on a drug therapy regimen be accommodated simultaneously. To do so requires a unified data model of the patient in which all possible states of each particular dimension are defined symbolically and numerically (mathematically) ordered. A unified patient data model would then allow for machine-based symbolic reasoning and automatic calculation of the most appropriate choices for drug therapy, including necessary dosage adjustments for each chosen drug. The decision on which drug to use in the first place, for example, is often based on a drug's efficacy in a specific situation, that is defined by subsets of all possible dimensions. For example, use of aspirin for stroke prevention in a patient with low blood platelet count is dramatically different from a person who has a normal platelet count. Finally, such a system must consider certain economic factors, such as whether or not the medication chosen is covered by a patient's health insurance provider (referred to as the approved drug formulary).